Spatial and temporal information for semantic segmentation

ABSTRACT

Systems and methods for segmenting an image using a convolutional neural network are described herein. A convolutional neural network (CNN) comprises an encoder-decoder architecture, and may comprise one or more Long Short Term Memory (LSTM) layers between the encoder and decoder layers. The LSTM layers provide temporal information in addition to the spatial information of the encoder-decoder layers. A subset of a sequence of images is input into the encoder layer of the CNN and a corresponding sequence of segmented images is output from the decoder layer. In some embodiments, the one or more LSTM layers may be combined in such a way that the CNN is predictive, providing predicted output of segmented images. Though the CNN provides multiple outputs, the CNN may be trained from single images or by generation of noisy ground truth datasets. Segmenting may be performed for object segmentation or free space segmentation.

CROSS REFERENCE TO RELATED APPLICATIONS

This is a continuation application which claims priority to commonlyassigned, U.S. patent application Ser. No. 15/409,841, filed Jan. 19,2017, which patent application claims priority from U.S. ProvisionalPatent Application No. 62/422,009, filed Nov. 14, 2016. Application Ser.Nos. 15/409,841 and 62/422,009 are fully incorporated herein byreference.

BACKGROUND

Image segmentation is often used as a method for partitioning an imageinto different segments, or super-pixels, to provide a more meaningfulrepresentation of the image. As one example, an image may be segmentedso as to uniquely identify objects within the image.

Image segmentation can be used in a number of different applications.Considering an image of a scene in an environment, a segmented imageassociated with the scene may contain a representation of every objectlocated in the environment. The presence and/or locations of the objectscontained in the segmented image may, in turn, be used for obstacleavoidance, object detection and tracking, or the like in systems usingsome aspects of machine vision.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is described with reference to the accompanyingfigures. In the figures, the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears. Theuse of the same reference numbers in different figures indicates similaror identical components or features.

FIGS. 1A and 1B illustrate exemplary image segmentation to produceimages segmented for objects and for free space, respectively.

FIG. 2 illustrates an example architecture for a convolutional neuralnetwork (CNN) to produce segmented images.

FIG. 3 illustrates another example architecture for a convolutionalneural network (CNN) to produce segmented images.

FIG. 4 depicts a process for performing image segmentation using eitherconvolutional neural network (CNN) as illustrated in FIG. 2 or FIG. 3.

FIG. 5 depicts another exemplary embodiment of a process for performingimage segmentation on input images.

FIG. 6 depicts a process for performing an oversegmentation andpropagation to create noisy datasets.

FIG. 7 depicts a process for using either convolutional neural network(CNN) as illustrated in FIG. 2 or FIG. 3 to control an autonomousvehicle.

FIG. 8 depicts an example block diagram for a computer systemimplementing the techniques described herein.

DETAILED DESCRIPTION

The following detailed description is merely exemplary in nature and isnot intended to limit the described embodiments or the application anduses of the described embodiments. As used herein, the word “exemplary”or “illustrative” means “serving as an example, instance, orillustration.” Any implementation described herein as “exemplary” or“illustrative” is not necessarily to be construed as preferred oradvantageous over other implementations. All of the implementationsdescribed below are exemplary implementations provided to enable personsskilled in the art to make or use the embodiments of the disclosure andare not intended to limit the scope of the disclosure, which is definedby the claims. Furthermore, there is no intention to be bound by anyexpressed or implied theory presented in the preceding technical field,background, brief summary or the following detailed description. It isalso to be understood that the specific devices and processesillustrated in the attached drawings, and described in the followingspecification, are simply exemplary embodiments of the inventiveconcepts defined in the appended claims. Hence, specific dimensions andother physical characteristics relating to the embodiments disclosedherein are not to be considered as limiting, unless the claims expresslystate otherwise.

The following detailed description is directed to systems, devices, andtechniques for segmenting a subset of images from a sequence of imagesusing temporal information. As discussed herein, image segmentation canbe used to provide a representation which is more meaningful than imagedata alone. For example, segmented images acquired from an image capturedevice onboard an autonomous vehicle can be used to inform theautonomous vehicle of the presence and/or locations of objects, such asother vehicles, pedestrians, and roads, in an environment. In turn, thisinformation can be used to generate trajectories and control the vehicleso as to avoid those obstacles, stay on a given path, or so on.

Though there are methods which segment an image algorithmically, thosemethods may not be fast or robust to different inputs. To overcome theselimitations, in some embodiments, machine learning is used to predict asegmented image from an input image. Machine learning generally refersto a broad class of such algorithms in which an output is generatedbased on learned parameters, which will be discussed in detail below. Insome embodiments, an example machine learning algorithm which can beused to generate the segmented image is a convolutional neural network,or CNN. CNNs are biologically inspired algorithms which pass input datathrough a series of connected layers to produce an output. Each layer ina CNN may comprise any number of layers, and may also comprise anotherCNN. The manner in which the various layers of a CNN are connected toone another is generally referred to as an architecture of the CNN. Thearchitecture for a CNN which produces segmented images will be discussedin detail below.

The improved architecture for a convolutional neural network, asdescribed herein, improves a functioning of a computing device byreducing processing time and/or increasing an accuracy of results in amachine learning context. For example, a CNN can incorporate one or morelong short term memory (LSTM) layers to associate temporal informationwith spatial information associated with a dataset, thereby improvingprocessing time and/or an accuracy of object identification. In anotherexample, incorporating one or more LSTM layers into the CNN providesmore accurate predictions of future events, such as predictions ofobject motion. Further, smaller datasets can be utilized for training aCNN by generating “noisy” datasets by propagating labels from anoversegmented ground truth image to one or more unlabeled images, forexample, which reduces an initial amount of data and processing fortraining. Further, by generating a larger noisy dataset from a smallerinitial dataset, an accuracy of the CNN can be increased for semanticsegmentation. These and other improvements to the functioning of thecomputer are discussed herein.

Exemplary Convolutional Neural Network (CNN) Architecture

In general, CNNs comprise multiple layers. Depending on the problem tobe solved, differing layers and connections between layers can be used.The architecture of the CNN refers to which layers are used and how theyare connected. As will be discussed in detail below, a CNN which hasbeen trained can form the basis of another CNN. In any embodiment, thelayers in the architecture can be selected based on the training datasetused, the complexity of the objects, and the like.

Each layer in a CNN architecture creates some association ofinformation. Here, segmentation of images is performed through the useof both spatial layers, as well as temporal layers. These spatial layersprovide spatial information of an image, for example comparison of pixelvalues of neighboring pixels, whether intensities, Red-Green-Blue (RGB),or the like. One such example of a spatial layer is an encoderarchitecture. Such an encoder architecture may comprise a previouslytrained CNN, such as, for example, a Visual Geometry Group (VGG) CNN.Inversely, a decoder layer may perform the inverse operation of anencoder layer, providing an image as output from spatial informationprovided.

An example CNN which can be used to segment an image may be referred toas an encoder-decoder architecture. As will be discussed in detailbelow, such a CNN comprises an encoder layer which is in turn connectedto a decoder layer. In some embodiments, a chronologically orderedsubset of a sequence of images is input into the encoder layer andcorresponding segmented images are output from a decoder layer. In someinstances, a subset may include some or all of a sequence of images.

In some instances, the CNN described herein can perform semanticsegmentation to segment images and provide classification and/oridentification of objects associated with the segmented portions of theimage.

In some instances, the CNN may use convolutional layers that filter atleast a portion of the input image for useful information. Theseconvolutional layers within the CNN have parameters that are learned sothat these filters are adjusted automatically to extract the most usefulinformation for the task at hand. For example, in a general objectrecognition task it might be most useful to filter information about theshape of an object (e.g., recognizing a car model may be based on ashape of the car). By way of another example, for a bird recognitiontask it might be more useful to extract information about the color ofthe bird (e.g., most birds have a similar shape but different colors;thus, color may be more useful to distinguish between birds). In someinstances, CNNs adjust automatically to find the best feature forperforming a particular task.

In some embodiments, temporal information is included in one or more ofthe layers to create a CNN which is capable of making better predictionsthan using spatial information alone. In some embodiments, one or moreLong Short Term Memory, or LSTM, layers may be placed between theencoder and decoder layers. In certain embodiments, multiple LSTM layersmay be connected together to provide better segmentation predictions. Insome instances, portions of the encoder layer may be directly connectedto the decoder layer, while in some instances, a LSTM layer may besituated in some or all paths between the encoder layer and the decoderlayer. In some instances, any LSTM layer may propagate informationforwards or backwards to any other layer in the CNN.

In one embodiment, the one or more LSTM layers comprise one or moreencoder LSTMs and one or more decoder LSTMs. Each encoder LSTM can beconnected to a subsequent encoder LSTM and/or a decoder LSTM. The firstencoder LSTM receives its input from the encoder layer. Each decoderLSTM can also be connected to each other, with the output of the finaldecoder LSTM being connected to the decoder layer. In such anarchitecture, output of the decoder layer may be predictive, forexample, that each output frame of the decoder layer corresponds to asegmented image which occurs chronologically after the last input imageinto the encoder layer. Such a configuration may be referred to as apredictive CNN. By way of an example, and without limitation, apredictive CNN may be utilized to receive a sequence of imagesrepresenting movement of an object, such as a car, and predict a futuremotion of the car based on the operations described herein.

Exemplary Training of the Convolutional Neural Network (CNN)

To produce a valid output, a CNN must first learn a set of parameters,or be “trained.” Training is accomplished by inputting a dataset intothe CNN, the dataset being associated with expected output values. Theseexpected output values may generally be referred to as “ground truth.”For example, a ground truth may include an identification of specificobjects in an image, as well as a semantic classification or label ofthe object (e.g., identifying and labeling an object as a car or abuilding). The accuracy of a CNN may be based on the amount of dataprovided in the training set. As such, an appropriate dataset to train aCNN to output segmented images would include a sequence of images havingknown, or previously determined, segments. In some instances, asdescribed herein, datasets can include one or more images representingreal-world scenes and may be annotated by hand or via one or morealgorithms to segment, detect, classify, and/or label objects in thedataset. In some instances, a dataset can include synthetic (e.g.,computer generated) data that include annotated objects or that has beenannotated by a computer algorithm. Training can be performed usingoffline and/or online data.

Loss functions can be used to adjust internal parameters of the CNNduring training. The loss functions are functions of the expected output(or ground truth) values for the dataset and values output by the CNN.Information contained in loss functions can be sent through the CNN asback propagations to adjust internal parameters, thereby tuning the CNNto provide valid outputs. All else being equal, the more data that isused to train a CNN, the more reliable the CNN may be (e.g., inproviding accurate segmentations and/or classifications).

One example of such a loss function which can be used to train a CNN tosegment images is the softmax function, though any other function ofinput images with expected, or ground truth, segmented images iscontemplated. Other exemplary loss functions include, but are notlimited to, support vector machine (SVM) loss, hinge loss, etc.

In some embodiments, ground truth segmented images are provided forevery output of the decoder layer. In some instances, it can be betterto have ground truth data for every frame in a dataset; however, somedatasets may not have ground truth for every frame. Therefore, in otherembodiments, it is possible to train the CNN using portions of datasetswhich only have a partial subset of ground truth segmented images. Inone embodiment, in those portions of a ground truth dataset having onlyone ground truth segmented image, the ground truth segmented image canbe associated with the final frame of the decoder layer output. Inanother embodiment, the CNN can be trained by providing an image with aground truth segmented image as the last frame input to the encoderlayer such that it is the first output of the decoder layer. In yetanother embodiment, one or more ground truth segmented images maycorrespond to any or all of the inputs and/or outputs to the CNN.

Even where a dataset does not contain ground truth segmented images forevery frame, it is still possible to provide the CNN with a trainingdataset comprising “noisy” ground truth segmented images. In one or moreembodiments, it is possible to create a “noisy” ground truth datasetfrom a single ground truth segmented image from a sequence of images. Insome embodiments, this is performed using spatio-temporal superpixels,such as those described in “Unsupervised Spatio-Temporal Segmentationwith Sparse Spectral Clustering,” by Ghafarianzadeh et al. As anexample, the image associated with the ground truth segmentation isfirst over-segmented. In some examples, that image is selected to be thecenter image in a sequence of images. Each segment in the over-segmentedimage is then labeled using corresponding ground truth segment imagelabels. Using the spatio-temporal superpixel representation, thoselabels are then propagated, both forward and backward, to some or allother images in the sequence to create “noisy” ground truth segmentedimages. Because some images in the sequence may contain information notavailable in the ground truth image, for example due to motion, portionsof those images may not contain a propagated label. Any number of thenoisy ground truth can then be used to train the CNN. Using the noisyground truth technique to train the CNN may be faster and provide higheraccuracies than other training methods described herein, or available inthe prior art.

In some embodiments, the CNN can be trained for object segmentation, forexample, by segmenting an input image based on the objects containedtherein. However, many other segmentations are contemplated. As anotherexample, the CNN can be trained to find “free spaces,” or areas in animage which would provide a suitable path for planning a trajectory foran autonomous vehicle. In such an embodiment, the free spaces defined inthe segmented image are then, in turn, used to generate trajectories andprovide commands to an autonomous vehicle to follow such a trajectory.Conversely, the CNN can be trained to find occupied spaces, or areas inan image which would not provide a suitable path for planning atrajectory for an autonomous vehicle.

Increasing the Amount of Training Data (Data Augmentation)

As described above, the accuracy of a CNN can be based at least in parton the amount of data provided in the training set, with more data or ahigher number of images in the training set often providing moreaccurate results. Because datasets have a limited number of images, itis possible to increase the amount of data provided to the CNN fortraining by perturbing the input data from a given training dataset.Various perturbations include mirroring a cropped portion, enlarging thearea for cropping by some amount (for example 20%), adding noise to theimage, resizing the image to some fixed dimensions (for example224×224), varying color, brightness, contrast, etc., and varying thelocations of the corners of the image. Additionally, by extending thedata set through this perturbation method, a smaller training data setcan be used, thereby requiring less memory storage space on a computer.More details are provided below with reference to FIGS. 1-8.

FIGS. 1A and 1B show examples of an image with corresponding outputsegmented images. Turning first to FIG. 1A, an example input image 100may correspond to an example object segmented image 110. As above,segmentation can be performed to determine a number of meaningfulrepresentations of an image. By segmenting input image 100 based onobjects present in the input image 100, the object segmented image 110can be produced. As illustrated, each different shade of objectsegmented image 110 corresponds to a different object (e.g., car,building, pedestrian, road marker, etc.). As such, it is possible todetermine locations, classifications, and presence or absence ofspecific objects in an image.

Similarly, FIG. 1B represents another possible segmentation schema.Again, an input image 100 is segmented. Unlike FIG. 1A, the input image100 may be segmented based on “free space,” or potential drivableregions. FIG. 1B illustrates one possible representation of a free spacesegmented image 120. There, the free space segmented image 120 mayprovide information (e.g., various shades) for confidence of navigablepathways. As shown, the lighter shade of free space segmented image 120corresponds to road surfaces which are not obstructed by objects, suchas other vehicles or pedestrians. Such a representation is important fordeveloping trajectories for autonomous vehicles so as to avoidcollisions.

FIG. 2 shows an architecture 200 for one embodiment of a convolutionalneural network (CNN) which is able to receive images and outputsegmented images. In one embodiment, the architecture 200 comprises anencoder layer 230. The encoder layer 230 may comprise a previouslytrained CNN, such as a Visual Group Geometry (VGG) CNN. The encoderlayer 230 can be configured to receive one or more input images 220,which have been labeled in chronological order from time 0 to time 3,with time 3 corresponding to a time after time 0. Though depicted asfour input images 220(3), 220(2), 220(1), and 220(0), the encoder layer230 may receive any number of input images 220, such as input images210(1) 210(2), 210(3), 210(4), 210(5), 210(6), 210(7), etc. The inputimages 220 may be selected as a subset of a sequence of images 210. Sucha sequence of input images may be generated from any image capturedevice, such as, for example, a video camera. In some instances, thesequence of input images may be generated as synthetic (e.g., computergenerated) data. Although the input images 220 are depicted in FIG. 2 ascorresponding to every other frame in the sequence of images 210, theinput images 220 may comprise every frame, or every nth frame (where nis a positive integer), of the sequence of images 210. The encoder layer230 may be configured to receive images which have Red-Green-Blue (RGB),intensity, or RGB-Depth information (e.g., including LIDAR orradar-based information).

After receiving the input images 220, the encoder layer 230 mayassociate spatial information with each of the input images 220 and maydirect an output of the encoder layer 230 to one or more Long Short TermMemory (LSTM) layers 240. Where more than one LSTM layer 240 is used,the output of each may be connected to the subsequent LSTM layerserially. That is, the output of a first LSTM layer may be an input to asecond LSTM layer, which in turn, can be connected to another LSTM layer(or a decoder layer, as described herein). The LSTM layers 240, in turn,associate temporal information with the input images 220. That is, theLSTM layers 240 can evaluate temporal features of the input images 220over many frames. In some instances, the LSTM layers 240 can learn oneor more dependencies or associations between data in multiple frames. Insome instances, the LSTM layers 240 can store and retrieve thisinformation (e.g., associations between frames, probabilisticinferences, etc.) over any period of time or any number of frames.Output of the one or more LSTM layers 240 can be connected to a decoderlayer 250. The decoder layer 250 may perform an inverse operation on thedata to produce a series of segmented images 260. Each of the segmentedimages 260 may correspond to the input images 220 such that the firstinput image 220 (e.g., image 220(0)), may correspond to the firstsegmented image 260 (e.g., segmented image 260(0)) output by the decoderlayer 250.

FIG. 3 illustrates an alternative architecture of a convolutional neuralnetwork. As opposed to architecture 200 as shown in FIG. 2, thearchitecture 300 in FIG. 3 may not have one or more serially connectedLSTM layers 240. Instead, FIG. 3 depicts an embodiment where the one ormore LSTM layers comprise one or more encoder LSTM layers 242(A), . . ., 242(N) and one or more decoder LSTM layers 244(A), . . . , 244(N). Insuch a configuration, subsequent encoder LSTM layers 242(A), . . . ,242(N) may be connected to one another, such that a hidden output ofLSTM layer 242(A) is input to a hidden input of a subsequent LSTM layer,and so on through LSTM layer 242(N). Similarly, subsequent decoder LSTMlayers 244(A), . . . , 244(N) may be connected to one another, such thata hidden output of LSTM layer 244(A) is input to a hidden input of asubsequent LSTM layer, and so on through LSTM layer 244(N). Further, insuch a configuration, each encoder LSTM layer (e.g., layer 242(A)through layer 242(N)) may be connected to a corresponding decoder LSTMlayer (e.g., layer 244(A) through layer 244(N)) such that output ofencoder LSTM layer 242(A) may be connected to the input of decoder LSTMlayer 244(A), output of encoder LSTM layer 242(B) may be connected tothe input of decoder LSTM layer 244(B), and so on. In some instances, anencoder LSTM layer (e.g., 242(A)) can map an input sequence of frames orinput data into a fixed length representation.

In such an embodiment as illustrated in FIG. 3, the CNN operates in apredictive manner. As shown, segmented images 260(4), 260(5), 260(6),and 260(7) are enumerated so as indicate that they are chronologicallyafter the input images 220(0), 220(1), 220(2), and 220(3). As such, theoutput of decoder layer 250 is predictive of future frames. For example,if the input images 220(0), 220(1), 220(2), and 220(3) include arepresentation of motion of a vehicle, the segmented images 260(4),260(5), 260(6), and 260(7) may include a prediction of future motion ofthe vehicle based upon the representations provided in the input images220. These and other predictions are within the scope of the disclosure,as described herein.

FIGS. 4-7 illustrate example processes in accordance with embodiments ofthe disclosure. These processes are illustrated as logical flow graphs,each operation of which represents a sequence of operations that can beimplemented in hardware, software, or a combination thereof. In thecontext of software, the operations represent computer-executableinstructions stored on one or more computer-readable storage media that,when executed by one or more processors, perform the recited operations.Generally, computer-executable instructions include routines, programs,objects, components, data structures, and the like that performparticular functions or implement particular abstract data types. Theorder in which the operations are described is not intended to beconstrued as a limitation, and any number of the described operationscan be combined in any order and/or in parallel to implement theprocesses.

FIG. 4 briefly illustrates a process 400 for segmented image predictionusing any of the architectures described in detail above. For example,some or all of the process 400 can be performed by one or morecomponents in the architectures 200 and 300, or in the computing system800, as described below.

At 410, images are received from an image capture device, such as avideo camera. In some instances, the images can include any number ofchannels, such as RGB channels, LIDAR channels, etc. At 420, a subset ofthose images are selected. The subset can selected such that each imageequally spaced from each other in the sequence. As an example, thesubset of images comprises every third frame from the sequence ofimages. At 430, the subset of images is input into an encoder layer. Theencoder layer then provides spatial information about the subset as anoutput. At 440, output from the encoder layer is received at one or moreLSTM layers, whether serially connected or as described in FIG. 3. TheseLSTM layers associate temporal information with the subset of images. At450, output from the LSTM layers are received by one or more decoderlayers. The one or more decoder layers decode the spatial and temporalinformation to image information. At 460, segmented images are outputfrom the decoder layer. An example of a segmented image that may beoutput by the operation 460 is illustrated in FIG. 1 as segmented images110 and 120.

FIG. 5 depicts another exemplary embodiment of a process 500 forperforming image segmentation on input images. For example, some or allof the process 500 can be performed by one or more components in thearchitectures 200 and 300, or in the computing system 800, as describedbelow. In some instances, the process 500 is performed by aconvolutional neural network including an encoder layer, a long shortterm memory (LSTM) layer, and/or a decoder layer, as described herein.

At 510, the operation may include receiving a plurality of input images.In some instances, the plurality of input images may be received fromone or more image sensors. In some instances, the plurality of inputimages may include additional sensor data, such as data from one or moreLIDAR sensors, radar sensors, etc. In some instances, the plurality ofinput images may be received from a perception system associated with anautonomous vehicle.

At 520, the operation may include selecting at least a portion of theplurality of input images for performing semantic segmentation. Forexample, it may be the case that a subset of a stream of image data maybe extracted and input to the convolutional neural network for imagesegmentation. In some instances, to reduce an amount of processing,frames of the plurality of input image may be selected at regularintervals. For example, for video data including 30 frames per second,the operation 520 may include selecting every third, fourth, fifth,etc., frame to reduce an amount of frames to be processed.

At 530, the operation may include determining, via the encoder layer,spatial information associated with the at least the portion of theplurality of the input images. For example, the encoder layer may applyone or more convolutional transformations to the selected data todetermine spatial information regarding objects in the selected data. Insome instances, the spatial information may be based on color datarepresenting objects in the selected data, etc.

At 540, the operation may include determining, via the LSTM layer,temporal information associated with the at least the portion of theplurality of the input images. For example, the LSTM layer may includeany number of LSTM layers, which may propagate temporal information ofobjects within the selected data forwards or backwards to various LSTMcells to determine temporal information associated with the selecteddata.

A 550, the operation may include determining a semantic classificationassociated with at least one object represented in the at least theportion of the plurality of input images, the determining the semanticclassification based at least in part on the spatial information and thetemporal information. For example, a semantic classification may includeidentifying a class or type of object, such as a car, pedestrian,building, a road, a tree, etc. It may be understood in the context ofthis disclosure that the convolutional neural network can be trainedusing training data (e.g., annotated images identifying objects in theimages), which may in turn provide semantic segmentation of objects inthe selected images.

At 560, the operation may include outputting at least one segmentedimage, the at least one segmented image associated with the semanticclassification. For example, objects may be segmented (e.g., pixelsassociated with objects can be identified spatially and labeled with aclassification of the object). For example, the at least one segmentedimage may be segmented to identify free space and occupied space,whereby free space corresponds to an area where an autonomous vehiclemay operate. For example, in a scene of a parking lot (e.g., asillustrated in FIG. 1), the free space may indicate areas of the parkinglot unoccupied by pedestrians, buildings, and other vehicles. In someinstances, the at least one segmented image may be provided to a plannersystem to generate a trajectory for the autonomous vehicle based on thefree space identified in the at least one segmented image. In someembodiments, the semantic classification may correspond to a defectiveproduct, for example, whereby the at least one segmented image may beprovided to a robot or machine to manipulate the defective product, suchas during quality control in a product assembly line.

Exemplary Training

Returning briefly to FIGS. 2 and 3, in order to train the CNN asillustrated in either embodiment, a ground truth segmented image dataset can be used. Ideally, every segmented image 260 is associated with aground truth segmented image. The CNN may then be trained based on thesoftmax function (or any loss function), incorporating data of thesegmented images 260 and the ground truth segmented images whichcorrespond to them. As mentioned above, there may not be enough groundtruth segmented data for every segmented image 260 to be associated witha ground truth segmented image. In those situations, the CNN may betrained based on one or more ground truth segmented images. In someembodiments, a single ground truth segmented image is compared with thelast output segmented image 260 of the CNN for training (e.g., eithersegmented image 260(3) in FIG. 2 or segmented image 260(7) of FIG. 3).In another embodiment, the last input image 220 and the first outputsegmented image 260 (e.g., input image 220(3) and segmented image 260(0)of FIG. 2, respectively) are the image and segmented image associatedwith the ground truth segmented image.

FIG. 6 shows a process 600 for creating a “noisy” ground truth data setfrom a dataset which has a portion of ground truth segmented imagescorresponding to images in the training set. In those embodiments, eventhough only a single ground truth segmented image is provided fortraining, “noisy” ground truth segmented images can be generated so asto increase the amount of training data. At 610, an image in a datasetcorresponding to a ground truth segmented image is selected. In someembodiments, that image is a middle image in a sequence, while in someinstances, the ground truth image may be a first image or a last imagein a sequence, or any position in a sequence. For example, a dataset of30 images may be chosen from a larger dataset such that the image havinga ground truth segmented image is the 15th image in the sequence. At620, the image having a corresponding ground truth segmented image isoversegmented to produce an oversegmented image. In some instances, anoversegmented image corresponds to objects being segmented within animage being segmented themselves or fractured into subcomponents. At630, each segment of the oversegmented image is associated with a labelof the ground truth segmented image. In some instances, examples oflabels include, but are not limited to representations of objects suchas ground, construction, objects, sky, human, vehicle, etc. Of course,any number of labels may be applied to identify segments in theoversegmented image. At 640, the labels are propagated to other imagesin the training data set using spatio-temporal super pixels to create a“noisy” ground truth dataset. For example, for a ground truth image withan index N, the labels may be propagated forward (e.g., to an imagehaving index N+M₁, where M₁ is a positive integer), and/or propagatedbackwards (e.g., to an image having index N−M₂, where M₂ is a positiveinteger). For a particular image (e.g., image(N)), propagating labelsmay include identifying similar features in related images (e.g.,image(N+M₁) or image(N−M₂)) and associating the ground truth label withaspects of the images. In some instances, for one or more pixels orregions in a related image that are not present in the ground truthannotations (or that emerge or disappear in the related images), a voidlabel can be applied. The noisy ground truth dataset can then be used totrain the CNN.

FIG. 7 illustrates an exemplary process 700 to control an autonomousvehicle based at least in part on a convolutional neural network (CNN)as described in any embodiment above. At 710, a subset of images from asequence of images is input into the CNN trained to segment based onfree space (e.g., drivable or navigable space) in the input image. As anexample, the sequence of images may be generated from an image capturesystem (e.g., a perception system) onboard an autonomous vehicle. Insome instances, the image capture system may include any number ofsensors, including but not limited to image sensors, LIDAR, radar, etc.At 720, the subset of images is segmented to create a set of free spacesegmented images. At 730, the free space segmented images are input intoa planner system, to generate a trajectory. In some instances, theplanner system may be incorporated into a computing system to receivefree space segmented images and to generate a trajectory based at leastin part on the free space segmented image. At 740, a sequence ofcommands is generated to control an autonomous vehicle to drive alongthe trajectory generated in 730. In some instances, the trajectorygenerated in operation 730 may constrain the operation of the autonomousvehicle to operate within the free space segmented in the operation 720.Further, the commands generated in the operation 740 can be relayed to acontroller onboard an autonomous vehicle to control the autonomousvehicle to drive the trajectory. Although discussed in the context of anautonomous vehicle, the process 700, and the techniques and systemsdescribed herein, can be applied to a variety systems utilizing machinevision.

Exemplary Computerized System

Turning briefly to FIG. 8, a computerized system 800 is depicted as anexample computerized system on which the disclosures may be implementedin whole or in part. The computerized system 800 depicts a computersystem 810 that comprises a storage 820, one or more processor(s) 830, amemory 840, and an operating system 850. The storage 820, theprocessor(s) 830, the memory 840, and the operating system 850 may becommunicatively coupled over a communication infrastructure 860.Optionally, the computer system 810 may interact with a user, orenvironment, via input/output (I/O) device(s) 870, as well as one ormore other computing devices over a network 880, via the communicationinfrastructure 850. The operating system 850 may interact with othercomponents to control one or more applications 890.

The systems and methods described herein can be implemented in softwareor hardware or any combination thereof. The systems and methodsdescribed herein can be implemented using one or more computing deviceswhich may or may not be physically or logically separate from eachother. The methods may be performed by components arranged as eitheron-premise hardware, on-premise virtual systems, or hosted-privateinstances. Additionally, various aspects of the methods described hereinmay be combined or merged into other functions.

An exemplary computerized system for implementing the systems andmethods described herein is illustrated in FIG. 8. A processor orcomputer system can be configured to particularly perform some or all ofthe methods described herein. In some embodiments, the methods can bepartially or fully automated by one or more computers or processors. Thesystems and methods described herein may be implemented using acombination of any of hardware, firmware and/or software. The presentsystems and methods described herein (or any part(s) or function(s)thereof) may be implemented using hardware, software, firmware, or acombination thereof and may be implemented in one or more computersystems or other processing systems. In some embodiments, theillustrated system elements could be combined into a single hardwaredevice or separated into multiple hardware devices. If multiple hardwaredevices are used, the hardware devices could be physically locatedproximate to or remotely from each other. The embodiments of the methodsdescribed and illustrated are intended to be illustrative and not to belimiting. For example, some or all of the steps of the methods can becombined, rearranged, and/or omitted in different embodiments.

In one exemplary embodiment, the systems and methods described hereinmay be directed toward one or more computer systems capable of carryingout the functionality described herein. Example computing devices maybe, but are not limited to, a personal computer (PC) system running anyoperating system such as, but not limited to, OS X™, iOS™, Linux™,Android™, and Microsoft™ Windows™ However, the systems and methodsdescribed herein may not be limited to these platforms. Instead, thesystems and methods described herein may be implemented on anyappropriate computer system running any appropriate operating system.Other components of the systems and methods described herein, such as,but not limited to, a computing device, a communications device, mobilephone, a smartphone, a telephony device, a telephone, a personal digitalassistant (PDA), a personal computer (PC), a handheld PC, an interactivetelevision (iTV), a digital video recorder (DVD), client workstations,thin clients, thick clients, proxy servers, network communicationservers, remote access devices, client computers, server computers,routers, web servers, data, media, audio, video, telephony or streamingtechnology servers, etc., may also be implemented using a computingdevice. Services may be provided on demand using, e.g., but not limitedto, an interactive television (iTV), a video on demand system (VOD), andvia a digital video recorder (DVR), or other on demand viewing system.

The system may include one or more processors. The processor(s) may beconnected to a communication infrastructure, such as but not limited to,a communications bus, cross-over bar, or network, etc. The processes andprocessors need not be located at the same physical locations. In otherwords, processes can be executed at one or more geographically distantprocessors, over for example, a LAN or WAN connection. Computing devicesmay include a display interface that may forward graphics, text, andother data from the communication infrastructure for display on adisplay unit.

The computer system may also include, but is not limited to, a mainmemory, random access memory (RAM), and a secondary memory, etc. Thesecondary memory may include, for example, a hard disk drive and/or aremovable storage drive, such as a compact disc drive CD-ROM, etc. Theremovable storage drive may read from and/or write to a removablestorage unit. As may be appreciated, the removable storage unit mayinclude a computer usable storage medium having stored therein computersoftware and/or data. In some embodiments, a machine-accessible mediummay refer to any storage device used for storing data accessible by acomputer. Examples of a machine-accessible medium may include, e.g., butnot limited to: a magnetic hard disk; a floppy disk; an optical disk,like a compact disc read-only memory (CD-ROM) or a digital versatiledisc (DVD); a magnetic tape; and/or a memory chip, etc.

The processor may also include, or be operatively coupled to communicatewith, one or more data storage devices for storing data. Such datastorage devices can include, as non-limiting examples, magnetic disks(including internal hard disks and removable disks), magneto-opticaldisks, optical disks, read-only memory, random access memory, and/orflash storage. Storage devices suitable for tangibly embodying computerprogram instructions and data can also include all forms of non-volatilememory, including, for example, semiconductor memory devices, such asEPROM, EEPROM, and flash memory devices; magnetic disks such as internalhard disks and removable disks; magneto-optical disks; and CD-ROM andDVD-ROM discs. The processor and the memory can be supplemented by, orincorporated in, ASICs (application-specific integrated circuits).

The processing system can be in communication with a computerized datastorage system. The data storage system can include a non-relational orrelational data store, such as a MySQL™ or other relational database.Other physical and logical database types could be used. The data storemay be a database server, such as Microsoft SQL Server™, Oracle™, IBMDB2™, SQLITE™, or any other database software, relational or otherwise.The data store may store the information identifying syntactical tagsand any information required to operate on syntactical tags. In someembodiments, the processing system may use object-oriented programmingand may store data in objects. In these embodiments, the processingsystem may use an object-relational mapper (ORM) to store the dataobjects in a relational database. The systems and methods describedherein can be implemented using any number of physical data models. Inone example embodiment, a relational database management system (RDBMS)can be used. In those embodiments, tables in the RDBMS can includecolumns that represent coordinates. In the case of economic systems,data representing companies, products, etc. can be stored in tables inthe RDBMS. The tables can have pre-defined relationships between them.The tables can also have adjuncts associated with the coordinates.

In alternative exemplary embodiments, secondary memory may include othersimilar devices for allowing computer programs or other instructions tobe loaded into computer system. Such devices may include, for example, aremovable storage unit and an interface. Examples of such may include aprogram cartridge and cartridge interface (such as, e.g., but notlimited to, those found in video game devices), a removable memory chip(such as, e.g., but not limited to, an erasable programmable read onlymemory (EPROM), or programmable read only memory (PROM) and associatedsocket), and other removable storage units and interfaces, which mayallow software and data to be transferred from the removable storageunit to computer system.

The computing device may also include an input device such as, but notlimited to, a voice input device, such as a microphone, touch screens,gesture recognition devices, such as cameras, other natural userinterfaces, a mouse or other pointing device such as a digitizer, and akeyboard or other data entry device. The computing device may alsoinclude output devices, such as but not limited to, a display, and adisplay interface. The computing device may include input/output (I/O)devices such as but not limited to a communications interface, cable andcommunications path, etc. These devices may include, but are not limitedto, a network interface card, and modems. Communications interface(s)may allow software and data to be transferred between a computer systemand one or more external devices.

In one or more embodiments, the computing device may be operativelycoupled to an automotive system. Such automotive system may be eithermanually operated, semi-autonomous, or fully autonomous. In such anembodiment, input and output devices may include one or more imagecapture devices, controllers, microcontrollers, and/or other processorsto control automotive functions such as, but not limited to,acceleration, braking, and steering. Further, communicationinfrastructure in such embodiments may also include a Controller AreaNetwork (CAN) bus.

In one or more embodiments, the computing device may be operativelycoupled to any machine vision based system. For example, such machinebased vision systems include but are not limited to manually operated,semi-autonomous, or fully autonomous industrial or agricultural robots,household robot, inspection system, security system, etc. That is, theembodiments described herein are not limited to one particular contextand may be applicable to any application utilizing machine vision.

In one or more embodiments, the present embodiments can be practiced inthe environment of a computer network or networks. The network caninclude a private network, or a public network (for example theInternet, as described below), or a combination of both. The network mayinclude hardware, software, or a combination of both.

From a telecommunications-oriented view, the network can be described asa set of hardware nodes interconnected by a communications facility,with one or more processes (hardware, software, or a combinationthereof) functioning at each such node. The processes caninter-communicate and exchange information with one another viacommunication pathways between them using interprocess communicationpathways. On these pathways, appropriate communications protocols areused.

An exemplary computer and/or telecommunications network environment inaccordance with the present embodiments may include nodes, which mayinclude hardware, software, or a combination of hardware and software.The nodes may be interconnected via a communications network. Each nodemay include one or more processes, executable by processors incorporatedinto the nodes. A single process may be run by multiple processors, ormultiple processes may be run by a single processor, for example.Additionally, each of the nodes may provide an interface point betweennetwork and the outside world, and may incorporate a collection ofsub-networks.

In an exemplary embodiment, the processes may communicate with oneanother through interprocess communication pathways supportingcommunication through any communications protocol. The pathways mayfunction in sequence or in parallel, continuously or intermittently. Thepathways can use any of the communications standards, protocols ortechnologies, described herein with respect to a communications network,in addition to standard parallel instruction sets used by manycomputers.

The nodes may include any entities capable of performing processingfunctions. Examples of such nodes that can be used with the embodimentsinclude computers (such as personal computers, workstations, servers, ormainframes), handheld wireless devices and wireline devices (such aspersonal digital assistants (PDAs), modem cell phones with processingcapability, wireless email devices including BlackBerry™ devices),document processing devices (such as scanners, printers, facsimilemachines, or multifunction document machines), or complex entities (suchas local-area networks or wide area networks) to which are connected acollection of processors, as described. For example, in the context ofthe present disclosure, a node itself can be a wide-area network (WAN),a local-area network (LAN), a private network (such as a Virtual PrivateNetwork (VPN)), or collection of networks.

Communications between the nodes may be made possible by acommunications network. A node may be connected either continuously orintermittently with communications network. As an example, in thecontext of the present disclosure, a communications network can be adigital communications infrastructure providing adequate bandwidth andinformation security.

The communications network can include wireline communicationscapability, wireless communications capability, or a combination ofboth, at any frequencies, using any type of standard, protocol ortechnology. In addition, in the present embodiments, the communicationsnetwork can be a private network (for example, a VPN) or a publicnetwork (for example, the Internet).

A non-inclusive list of exemplary wireless protocols and technologiesused by a communications network may include Bluetooth™, general packetradio service (GPRS), cellular digital packet data (CDPD), mobilesolutions platform (MSP), multimedia messaging (MMS), wirelessapplication protocol (WAP), code division multiple access (CDMA), shortmessage service (SMS), wireless markup language (WML), handheld devicemarkup language (HDML), binary runtime environment for wireless (BREW),radio access network (RAN), and packet switched core networks (PS-CN).Also included are various generation wireless technologies. An exemplarynon-inclusive list of primarily wireline protocols and technologies usedby a communications network includes asynchronous transfer mode (ATM),enhanced interior gateway routing protocol (EIGRP), frame relay (FR),high-level data link control (HDLC), Internet control message protocol(ICMP), interior gateway routing protocol (IGRP), internetwork packetexchange (IPX), ISDN, point-to-point protocol (PPP), transmissioncontrol protocol/internet protocol (TCP/IP), routing informationprotocol (RIP) and user datagram protocol (UDP). As skilled persons willrecognize, any other known or anticipated wireless or wireline protocolsand technologies can be used.

Embodiments of the present disclosure may include apparatuses forperforming the operations herein. An apparatus may be speciallyconstructed for the desired purposes, or it may comprise a generalpurpose device selectively activated or reconfigured by a program storedin the device.

In one or more embodiments, the present embodiments are embodied inmachine-executable instructions. The instructions can be used to cause aprocessing device, for example a general-purpose or special-purposeprocessor, which is programmed with the instructions, to perform thesteps of the present disclosure. Alternatively, the steps of the presentdisclosure can be performed by specific hardware components that containhardwired logic for performing the steps, or by any combination ofprogrammed computer components and custom hardware components. Forexample, the present disclosure can be provided as a computer programproduct, as outlined above. In this environment, the embodiments caninclude a machine-readable medium having instructions stored on it. Theinstructions can be used to program any processor or processors (orother electronic devices) to perform a process or method according tothe present exemplary embodiments. In addition, the present disclosurecan also be downloaded and stored on a computer program product. Here,the program can be transferred from a remote computer (e.g., a server)to a requesting computer (e.g., a client) by way of data signalsembodied in a carrier wave or other propagation medium via acommunication link (e.g., a modem or network connection) and ultimatelysuch signals may be stored on the computer systems for subsequentexecution.

The methods can be implemented in a computer program product accessiblefrom a computer-usable or computer-readable storage medium that providesprogram code for use by or in connection with a computer or anyinstruction execution system. A computer-usable or computer-readablestorage medium can be any apparatus that can contain or store theprogram for use by or in connection with the computer or instructionexecution system, apparatus, or device.

A data processing system suitable for storing and/or executing thecorresponding program code can include at least one processor coupleddirectly or indirectly to computerized data storage devices such asmemory elements. Input/output (I/O) devices (including but not limitedto keyboards, displays, pointing devices, etc.) can be coupled to thesystem. Network adapters may also be coupled to the system to enable thedata processing system to become coupled to other data processingsystems or remote printers or storage devices through interveningprivate or public networks. To provide for interaction with a user, thefeatures can be implemented on a computer with a display device, such asan LCD (liquid crystal display), or another type of monitor fordisplaying information to the user, and a keyboard and an input device,such as a mouse or trackball by which the user can provide input to thecomputer.

A computer program can be a set of instructions that can be used,directly or indirectly, in a computer. The systems and methods describedherein can be implemented using programming languages such as CUDA,OpenCL, Flash™ JAVA™, C++, C, C#, Python, Visual Basic™, JavaScript™PHP, XML, HTML, etc., or a combination of programming languages,including compiled or interpreted languages, and can be deployed in anyform, including as a stand-alone program or as a module, component,subroutine, or other unit suitable for use in a computing environment.The software can include, but is not limited to, firmware, residentsoftware, microcode, etc. Protocols such as SOAP/HTTP may be used inimplementing interfaces between programming modules. The components andfunctionality described herein may be implemented on any desktopoperating system executing in a virtualized or non-virtualizedenvironment, using any programming language suitable for softwaredevelopment, including, but not limited to, different versions ofMicrosoft Windows™, Apple™ Mac™, iOS™, Unix™/X-Windows™, Linux™, etc.The system could be implemented using a web application framework, suchas Ruby on Rails.

Suitable processors for the execution of a program of instructionsinclude, but are not limited to, general and special purposemicroprocessors, and the sole processor or one of multiple processors orcores, of any kind of computer. A processor may receive and storeinstructions and data from a computerized data storage device such as aread-only memory, a random access memory, both, or any combination ofthe data storage devices described herein. A processor may include anyprocessing circuitry or control circuitry operative to control theoperations and performance of an electronic device.

The systems, modules, and methods described herein can be implementedusing any combination of software or hardware elements. The systems,modules, and methods described herein can be implemented using one ormore virtual machines operating alone or in combination with one other.Any applicable virtualization solution can be used for encapsulating aphysical computing machine platform into a virtual machine that isexecuted under the control of virtualization software running on ahardware computing platform or host. The virtual machine can have bothvirtual system hardware and guest operating system software.

The systems and methods described herein can be implemented in acomputer system that includes a back-end component, such as a dataserver, or that includes a middleware component, such as an applicationserver or an Internet server, or that includes a front-end component,such as a client computer having a graphical user interface or anInternet browser, or any combination of them. The components of thesystem can be connected by any form or medium of digital datacommunication such as a communication network. Examples of communicationnetworks include, e.g., a LAN, a WAN, and the computers and networksthat form the Internet.

One or more embodiments of the present disclosure may be practiced withother computer system configurations, including hand-held devices,microprocessor systems, microprocessor-based or programmable consumerelectronics, minicomputers, mainframe computers, etc. The systems andmethods described herein may also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a network.

The terms “computer program medium” and “computer readable medium” maybe used to generally refer to media such as but not limited to removablestorage drive, a hard disk installed in hard disk drive. These computerprogram products may provide software to computer system. The systemsand methods described herein may be directed to such computer programproducts.

References to “one embodiment,” “an embodiment,” “example embodiment,”“various embodiments,” etc., may indicate that the embodiment(s) of thepresent disclosure may include a particular feature, structure, orcharacteristic, but not every embodiment necessarily includes theparticular feature, structure, or characteristic. Further, repeated useof the phrase “in one embodiment,” or “in an exemplary embodiment,” donot necessarily refer to the same embodiment, although they may.

In the description and claims, the terms “coupled” and “connected,”along with their derivatives, may be used. It should be understood thatthese terms may be not intended as synonyms for each other. Rather, inparticular embodiments, “connected” may be used to indicate that two ormore elements are in direct physical or electrical contact with eachother. “Coupled” may mean that two or more elements are in directphysical or electrical contact. However, “coupled” may also mean thattwo or more elements are not in direct contact with each other, but yetstill co-operate or interact with each other.

An algorithm may be here, and generally, considered to be aself-consistent sequence of acts or operations leading to a desiredresult. These include physical manipulations of physical quantities.Usually, though not necessarily, these quantities take the form ofelectrical or magnetic signals capable of being stored, transferred,combined, compared, and otherwise manipulated. It has proven convenientat times, principally for reasons of common usage, to refer to thesesignals as bits, values, elements, symbols, characters, terms, numbersor the like. It should be understood, however, that all of these andsimilar terms are to be associated with the appropriate physicalquantities and are merely convenient labels applied to these quantities.

Unless specifically stated otherwise, it may be appreciated thatthroughout the specification terms such as “processing,” “computing,”“calculating,” “determining,” or the like, refer to the action and/orprocesses of a computer or computing system, or similar electroniccomputing device, that manipulate and/or transform data represented asphysical, such as electronic, quantities within the computing system'sregisters and/or memories into other data similarly represented asphysical quantities within the computing system's memories, registers orother such information storage, transmission or display devices.

In a similar manner, the term “processor” may refer to any device orportion of a device that processes electronic data from registers and/ormemory to transform that electronic data into other electronic data thatmay be stored in registers and/or memory. As non-limiting examples,“processor” may be a Central Processing Unit (CPU) or a GraphicsProcessing Unit (GPU). A “computing platform” may comprise one or moreprocessors. As used herein, “software” processes may include, forexample, software and/or hardware entities that perform work over time,such as tasks, threads, and intelligent agents. Also, each process mayrefer to multiple processes, for carrying out instructions in sequenceor in parallel, continuously or intermittently. The terms “system” and“method” are used herein interchangeably insofar as the system mayembody one or more methods and the methods may be considered as asystem.

While one or more embodiments have been described, various alterations,additions, permutations and equivalents thereof are included within thescope of the disclosure.

In the description of embodiments, reference is made to the accompanyingdrawings that form a part hereof, which show by way of illustrationspecific embodiments of the claimed subject matter. It is to beunderstood that other embodiments may be used and that changes oralterations, such as structural changes, may be made. Such embodiments,changes or alterations are not necessarily departures from the scopewith respect to the intended claimed subject matter. While the stepsherein may be presented in a certain order, in some cases the orderingmay be changed so that certain inputs are provided at different times orin a different order without changing the function of the systems andmethods described. The disclosed procedures could also be executed indifferent orders. Additionally, various computations that are hereinneed not be performed in the order disclosed, and other embodimentsusing alternative orderings of the computations could be readilyimplemented. In addition to being reordered, the computations could alsobe decomposed into sub-computations with the same results.

Although the discussion above sets forth example implementations of thedescribed techniques, other architectures may be used to implement thedescribed functionality, and are intended to be within the scope of thisdisclosure. Furthermore, although specific distributions ofresponsibilities are defined above for purposes of discussion, thevarious functions and responsibilities might be distributed and dividedin different ways, depending on circumstances.

Furthermore, although the subject matter has been described in languagespecific to structural features and/or methodological acts, it is to beunderstood that the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described. Rather,the specific features and acts are disclosed as exemplary forms ofimplementing the claims.

Example Clauses

A. An example system comprises:

-   -   one or more processors; and    -   one or more computer readable storage media communicatively        coupled to the one or more processors and storing instructions        that are executable by the one or more processors to:        -   receive a plurality of images from an image capture device;        -   transmit the plurality of images into a convolutional neural            network; and        -   generate a plurality of output images, an output image of            the plurality of output images including segmentation            information,    -   wherein the convolutional neural network comprises:        -   one or more encoder layers configured to receive, as a            plurality of input images, at least a portion of the            plurality of images and configured to output an encoded            output,        -   one or more long short term memory (LSTM) layers, the one or            more LSTM layers configured to receive the encoded output            and configured to output an LSTM output, and        -   one or more decoder layers configured to receive the LSTM            output and configured to output the plurality of output            images.

B. The system of example A, wherein the segmentation information isassociated with a representation of free space in the output image, and

-   -   wherein the instructions are further executable by the one or        more processors to transmit the output image to a planner system        as an input to generate a trajectory for an autonomous vehicle        to navigate within at least a portion of the free space        associated with the output image.

C. The system of examples A or B, wherein the one or more LSTM layerscomprise at least a first LSTM layer and a second LSTM layer,

-   -   the first LSTM layer configured to receive as first input the        encoded output from the one or more encoder layers and        configured to output a first LSTM output to the second LSTM        layer, and    -   the second LSTM layer configured to receive as second input the        first LSTM output from the first LSTM layer and configured to        output the LSTM output to at least one of a third LSTM layer or        to the one or more decoder layers.

D. The system of any one of example A through example C, wherein theinstructions are further executable by the one or more processors to:

-   -   determine, via the one or more encoder layers, spatial        information associated with the plurality of input images;    -   determine, via the one or more LSTM layers, temporal information        associated with the plurality of input images; and    -   associate, via the one or more decoder layers, the spatial        information with the temporal information to generate the        plurality of output images.

E. The system of any one of example A through example C, wherein theinstructions are further executable by the one or more processors togenerate semantic information associated with the segmentationinformation, the semantic information semantically classifying at leasta portion of pixels in the output image.

F. The system of any one of example A through example C, wherein theplurality of output images includes at least one prediction of futuremotion of at least one object associated with the plurality of inputimages.

G. The system of any one of example A through example C, wherein theinstructions are further executable by the one or more processors to:

-   -   receive a training data set, a first image of the training data        set including ground truth information, the ground truth        information including at least one label associated with an        object represented in the first image;    -   propagate the at least one label to at least a second image of        the training data set to generate a noisy training data set        including the first image and the second image, the second image        not including the ground truth information; and    -   train the convolutional neural network based at least in part on        the noisy training data set.

H. An example method for performing semantic segmentation in aconvolutional neural network including one or more encoder layers, oneor more long short term memory (LSTM) layers, and one or more decoderlayers, the method comprising:

-   -   receiving a plurality of input images;    -   selecting a subset of the plurality of the input images for the        semantic segmentation;    -   determining, via the one or more encoder layers, spatial        information associated with the subset of the plurality of the        input images;    -   determining, via the one or more LSTM layers, temporal        information associated with the subset of the plurality of the        input images;    -   determining a semantic classification associated with at least        one object represented in the subset of the plurality of input        images based at least in part on the spatial information and the        temporal information; and    -   outputting at least one segmented image, the at least one        segmented image associated with the semantic classification.

I. The method of example H, further comprising providing the at leastone segmented image to a planner system for generating a trajectoryassociated with an autonomous vehicle.

J. The method of example H or example I, wherein the one or more LSTMlayers include a plurality of LSTM layers, wherein an output of a firstLSTM layer of the plurality of LSTM layers is provided as input to asecond LSTM layer of the plurality of LSTM layers.

K. The method of any one of example H through example J, furthercomprising:

-   -   determining a predictive output based on the subset of the        plurality of the input images; and    -   outputting a plurality of segmented images, the plurality of        segmented images including the predictive output representing at        least one prediction of future motion of an object represented        in the subset of the plurality of the input images.

L. The method of any one of example H through example J, furthercomprising:

-   -   spatially segmenting an image of the subset of the plurality of        the input images to identify free space represented in the        image; and    -   generating a trajectory for an autonomous vehicle based at least        in part on the free space.

M. The method of any one of example H through example J, furthercomprising:

-   -   receiving, at the one or more decoder layers, an output from the        one or more LSTM layers; and    -   generating, via the one or more decoder layers, the at least one        segmented image based at least in part on the output from the        one or more LSTM layers.

N. The method of any one of example H through example J, furthercomprising training the convolutional neural network using noisytraining data, wherein at least a portion of first ground truthinformation associated with a first image of the noisy training data isderived from a second image of the noisy training data associated withsecond ground truth information.

O. A system comprises:

-   -   one or more processors; and    -   one or more computer readable storage media communicatively        coupled to the one or more processors and storing instructions        that are executable by the one or more processors to:        -   receive a plurality of input images;        -   select a subset of the plurality of the input images for            semantic segmentation;        -   determine, via one or more encoder layers, spatial            information associated with the subset of the plurality of            the input images;        -   determine, via one or more long short term memory (LSTM)            layers, temporal information associated with the subset of            the plurality of the input images;        -   determine a semantic classification associated with at least            one object represented in the subset of the plurality of            input images based at least in part on the spatial            information and the temporal information; and        -   output at least one segmented image, the at least one            segmented image associated with the semantic classification.

P. The system of example O, wherein the instructions are furtherexecutable by the one or more processors to provide the at least onesegmented image to a planner system for generating a trajectoryassociated with an autonomous vehicle.

Q. The system of example O or example P, wherein the one or more LSTMlayers includes a plurality of LSTM layers, wherein an output of a firstLSTM layer of the plurality of LSTM layers is provided as input to asecond LSTM layer of the plurality of LSTM layers.

R. The system of any one of example O through example Q, wherein the oneor more LSTM layers includes a plurality of encoder LSTM layers and aplurality of decoder LSTM layers, wherein a first encoder LSTM layer ofthe plurality of encoder LSTM layers is operatively coupled to acorresponding first decoder of the plurality of decoder LSTM layers, andthe first encoder LSTM layer is operatively coupled to a subsequentencoder LSTM layer of the plurality of encoder LSTM layers.

S. The system of any one of example O through example Q, wherein theinstructions are further executable by the one or more processors to:

-   -   determine a predictive output based on the subset of the        plurality of the input images; and    -   output a plurality of segmented images, the plurality of        segmented images including the predictive output representing at        least one prediction of future motion of an object represented        in the subset of the plurality of the input images.

T. The system of any one of example O through example Q, wherein theinstructions are further executable by the one or more processors to:

-   -   spatially segment an image of the subset of the plurality of the        input images to identify free space represented in the image;        and    -   generate a trajectory for an autonomous vehicle based at least        in part on the free space.

U. The system of any one of example O through example Q, wherein theinstructions are further executable by the one or more processors to:

-   -   receive, at one or more decoder layers, an output from the one        or more LSTM layers; and    -   generate, via the one or more decoder layers, the at least one        segmented image based at least in part on the output from the        one or more LSTM layers.

What is claimed is:
 1. A system comprising: one or more processors; andone or more computer readable storage media communicatively coupled tothe one or more processors and storing instructions that are executableby the one or more processors to: receive an image from an image capturedevice, wherein the image is associated with a first time; input theimage into a convolutional neural network; and generate a predictivesegmented image, wherein the predictive segmented image is associatedwith a second time after the first time and comprises predictivesemantic segmentation information identifying, as an identified object,at least one of a car, a building, a pedestrian, a road marker, or freespace, wherein the predictive segmented image comprises a prediction ofa future location of the identified object associated with thepredictive segmented image, wherein the convolutional neural networkcomprises: an encoder layer configured to receive the image andconfigured to output an encoded output, a long short term memory (LSTM)layer configured to receive the encoded output and configured to outputa neural network output, the LSTM layer comprising a first LSTM encoderlayer coupled to a second LSTM encoder layer and a LSTM decoder layer,and a decoder layer configured to receive the neural network output andconfigured to output the predictive segmented image.
 2. The system ofclaim 1, wherein the predictive semantic segmentation informationidentifies a representation of the free space in the predictivesegmented image, and wherein the instructions are further executable bythe one or more processors to transmit the predictive segmented image toa planner system as an input to generate a trajectory for an autonomousvehicle to navigate within at least a portion of the free spaceassociated with the predictive segmented image.
 3. The system of claim1, wherein the instructions are further executable by the one or moreprocessors to: determine, via the encoder layer, spatial informationassociated with the image; determine, via the LSTM layer, temporalinformation associated with the image; and associate, via the decoderlayer, the spatial information with the temporal information to generatethe predictive segmented image.
 4. The system of claim 1, wherein thepredictive semantic segmentation information semantically classifies atleast a portion of pixels in the predictive segmented image.
 5. Thesystem of claim 1, wherein the instructions are further executable bythe one or more processors to: receive a training data set, a firsttraining image of the training data set comprising ground truthinformation, the ground truth information including at least one labelassociated with an object represented in the first training image;propagate the at least one label to at least a second training image ofthe training data set to generate a noisy training data set comprisingthe first training image and the second training image, the secondtraining image not comprising the ground truth information; and trainthe convolutional neural network based at least in part on the noisytraining data set.
 6. The system of claim 1, wherein a first pixelrepresenting the identified object is associated with the predictivesemantic segmentation information and a second pixel representing theidentified object is associated with the predictive semanticsegmentation information.
 7. The system of claim 1, wherein the encoderlayer comprises a trained convolutional neural network.
 8. The system ofclaim 1, wherein: an output of the first LSTM encoder layer is receivedas input by the second LSTM encoder layer and by the LSTM decoder layer.9. The system of claim 1, wherein: the LSTM decoder layer is a firstLSTM decoder layer; and the LSTM layer further comprises: a second LSTMdecoder layer coupled to the first LSTM decoder layer and the secondLSTM encoder layer.
 10. A method for performing semantic segmentation ina convolutional neural network comprising an encoder layer, a long shortterm memory (LSTM) layer comprising a first LSTM encoder layer coupledto a second LSTM encoder layer and a LSTM decoder layer, and a decoderlayer, the method comprising: receiving an image, wherein the image isassociated with a first time; determining, via the encoder layer,spatial information associated with the image; determining, via the LSTMlayer, temporal information associated with the image; determining asemantic classification associated with an object represented in theimage based at least in part on the spatial information and the temporalinformation, the semantic classification comprising at least one of acar, a building, a pedestrian, a road marker, or free space; andoutputting a predictive segmented image, wherein: the predictivesegmented image is associated with a second time after the first time,the predictive segmented image comprises predictive segmentationinformation, the predictive segmented image is associated with thesemantic classification, and the predictive segmented image comprises aprediction of a future location of the object associated with thepredictive segmented image.
 11. The method of claim 10, furthercomprising providing the predictive segmented image to a planner systemfor generating a trajectory associated with an autonomous vehicle. 12.The method of claim 10, wherein the image is a first image, the methodfurther comprising: determining the predictive segmented image based atleast in part on the first image and a second image received at a thirdtime after the first time; and outputting a plurality of segmentedimages, wherein the predictive segmented image is one of the pluralityof segmented images.
 13. The method of claim 10, further comprising:spatially segmenting the image to identify the free space represented inthe image; and generating a trajectory for an autonomous vehicle basedat least in part on the free space.
 14. The method of claim 10, furthercomprising: receiving, at the decoder layer, an output from the LSTMlayer; and generating, via the decoder layer, the predictive segmentedimage based at least in part on the output from the LSTM layer.
 15. Themethod of claim 10, further comprising training the convolutional neuralnetwork using noisy training data, wherein at least a portion of firstground truth information associated with a first training image of thenoisy training data is derived from a second training image of the noisytraining data associated with second ground truth information.
 16. Oneor more non-transitory computer readable storage media storinginstructions that, when executed, cause one or more processors toperform operations comprising: receiving an image, wherein the image isassociated with a first time; determining, via an encoder layer, spatialinformation associated with the image; determining, via a long shortterm memory (LSTM) layer, temporal information associated with theimage, the LSTM layer comprising a first LSTM encoder layer coupled to asecond LSTM encoder layer and a LSTM decoder layer; determining asemantic classification associated with an object represented in theimage based at least in part on the spatial information and the temporalinformation, the semantic classification comprising at least one of acar, a building, a pedestrian, a road marker, or free space; andoutputting a predictive segmented image, wherein the predictivesegmented image is associated with the semantic classificationrepresenting a second time after the first time, and wherein thepredictive segmented image comprises a prediction of a future locationof the object associated with the predictive segmented image.
 17. Theone or more non-transitory computer readable storage media of claim 16,the operations further comprising providing the predictive segmentedimage to a planner system for generating a trajectory associated with anautonomous vehicle.
 18. The one or more non-transitory computer readablestorage media of claim 16, wherein the image is a first image, theoperations further comprising: determining the predictive segmentedimage based at least in part on the first image and a second imageassociated with a third time after the first time; and outputting aplurality of segmented images, wherein the predictive segmented image isone of the plurality of segmented images.
 19. The one or morenon-transitory computer readable storage media of claim 16, theoperations further comprising: spatially segmenting the image toidentify the free space represented in the image; and generating atrajectory for an autonomous vehicle based at least in part on the freespace.
 20. The one or more non-transitory computer readable storagemedia of claim 16, wherein the predictive segmented image comprises aclassification or an identification of an object associated with asegmented portion of the predictive segmented image.